{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='survived'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='survived'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
